Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters

Li, M.J.; Ng, Michael K.; Cheung, Yiu-ming; Huang, Jian

doi:10.1109/tkde.2008.88

Cited by 232 publications

(76 citation statements)

References 33 publications

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“…Many systems uses the recommender systems to form the dataset, the recommender system also uses the direct recommender algorithm to give out the data in the form of services [7]. Mostly the data set formed will not look in to the description value of the data instant it checks only for the migrant value of the data based on the usage of the data information.…”

Section: Data Set Formationsmentioning

confidence: 99%

“…To overcome these issues exact content mining techniques has been proposed and named as EPCRR (Enabled Pile Clustered exact content retrieval and repository). This work is similar but advanced web content mining which can be viewed as the use of data mining techniques with the advancement towards the automatic data retrieval [7]. It facilitates the web mining procedure namely usage, structure, content and user profiles.…”

mentioning

confidence: 99%

“…So it ranks the top most information with overcrowded and overlooked data as an output. The traditional informational retrieval techniques will deliver the content without the verification and overwhelmed data [7] [8]. The content is not devised using the characteristics and usage.…”

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confidence: 99%

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An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Manjula¹,

Chilambuchelvan²

2016

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Mining the content from an information database provides challenging solutions to the industry experts and researchers, due to the overcrowded information in huge data. In web searching, the information retrieved is not an appropriate, because it gives ambiguous information for the user query, and the user cannot get relevant information within the stipulated time. To overcome these issues, we propose a new methodology for information retrieval EPCRR by providing the top most exact information to the user, by using the collaborative clustered automated filter which makes use of the collaborative data set and filter works on the prediction by providing the highest ranking for the exact data retrieved. The retrieval works on the basis of recommendation of data which consists of relevant data set with highest priority from the cluster of data which is on high usage. In this work, we make use of the automated wrapper which works similar to the meta crawler functionality and it obtains the content in the semantic usage data format. Obtained information from the user to the agent will be ranked based on the Enabled Pile clustered data with respect to the metadata information from the agent and end-user. The information is given to the end-user with the top most ranking data within the stipulated time and the remaining top information will be moved to the data repository for future use. The data collected will remain stable based on the user preference and works on the intelligence system approach in which the user can choose any information under any instances and can be provided with suitable high range of exact content. In this approach, we find that the proposed algorithm has produced better results than existing work and it costs less online computation time.

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Section: Data Set Formationsmentioning

confidence: 99%

mentioning

confidence: 99%

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An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Manjula¹,

Chilambuchelvan²

2016

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“…Macy's merchandise pricing optimization application provides a classic example of reducing the cycle time for complex and large-scale analytical calculations from hours or even days to minutes or seconds [13]. The department store chain has been able to reduce the time to optimize pricing of its 73 million items for sale from over 27 hours to just over 1 hour.…”

Section: Big Data For Time Reductionmentioning

confidence: 99%

Big Data Analytics using Hadoop

Dhyani¹,

Barthwal²

2014

IJCA

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This paper is an effort to present the basic understanding of BIG DATA is and it's usefulness to an organization from the performance perspective. Along-with the introduction of BIG DATA, the important parameters and attributes that make this emerging concept attractive to organizations has been highlighted. The paper also evaluates the difference in the challenges faced by a small organization as compared to a medium or large scale operation and therefore the differences in their approach and treatment of BIG DATA. A number of application examples of implementation of BIG DATA across industries varying in strategy, product and processes have been presented. The second part of the paper deals with the technology aspects of BIG DATA for it's implementation in organizations. Since HADOOP has emerged as a popular tool for BIG DATA implementation, the paper deals with the overall architecture of HADOOP alongwith the details of it's various components. Further each of the components of the architecture has been taken up and described in detail.

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“…For instance, to find out who are similar to A in Table 1, the traditional recommendation system may use cosine similarity [2] or k-means algorithm [6], [7] to do the calculations. However, Finding similar users of this kind has its shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Finding Similar Users in Social Networks by Using the Neural-Based Skyline Region

Hou¹,

Chang²,

Chen³

et al. 2015

JCP

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Abstract:Finding similar users in a community network is a very important part of the recommendation system. Traditionally, algorithms of this kind are based on a single condition to search for similar users. In recent years, some scholars have proposed the popular multi-criteria algorithm Skyline Query to search for similar users. However, their proposed methods might have found users not similar to the target user and are subject to the problem of slow execution. To solve these issues, this paper introduces the concepts of neural-based skyline region and neural network. The simulation results have verified the effectiveness and execution efficiency of the target method.

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Agglomerative Fuzzy K-Means Clustering Algorithm with Selection of Number of Clusters

Cited by 232 publications

References 33 publications

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

An Approach for Content Retrieval from Web Pages Using Clustering Techniques

Big Data Analytics using Hadoop

Finding Similar Users in Social Networks by Using the Neural-Based Skyline Region

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